{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SfQFwotCuP9UWACcL9ZOpdl6fiPgYGJR01UXGMjOzOplV6wCS/gQYiIg3JJUu0jTnB3WN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\n1KEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2644a89fbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#载入数据\n",
    "data = np.genfromtxt(\"job.csv\",delimiter=',')\n",
    "x_data = data[1:,1]\n",
    "y_data = data[1:,2]\n",
    "plt.scatter(x_data,y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x_data = x_data[:,np.newaxis]\n",
    "y_data = y_data[:,np.newaxis]\n",
    "#创建并拟合模型\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression()\n",
    "model.fit(x_data,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dtbUwcSJ873tp2vChh7p1YmZtklssTbV48arjfT/9NLVOBgzw8b5m1qq4xbK+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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2644aa22b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "plt.plot(x_data,model.predict(x_data),'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#定义多项式回归，degree的值可以调节多项式的特征\n",
    "poly_reg = PolynomialFeatures(degree = 10) \n",
    "#特征处理\n",
    "x_poly = poly_reg.fit_transform(x_data)#degree = 3   x_poly  =  x^0  x^1  x^2  x^3\n",
    "#定义回归模型\n",
    "lin_reg = LinearRegression()\n",
    "#训练模型\n",
    "lin_reg.fit(x_poly,y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JN0naSFIfSWMlTZX0oKTeJetfJGmapCmSji4pH5rreF3SFSXlG0m6NW/ztKTtSl4bldef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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2644b3864a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#画图\n",
    "plt.plot(x_data,y_data,'b.')\n",
    "x_test = np.linspace(1,10,100)\n",
    "x_test = x_test[:,np.newaxis]\n",
    "plt.plot(x_test,lin_reg.predict(poly_reg.fit_transform(x_test)),c='r') #做预测的时候要传入已经处理过的值\n",
    "plt.title('Truth or Bluff (Polynomial Regression)')\n",
    "plt.xlabel('Position level')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
